Interactions of Estuarine Shoreline Infrastructure With Multiscale Sea Level Variability

被引:18
作者
Wang, Ruo-Qian [1 ]
Herdman, Liv M. [2 ]
Erikson, Li [2 ]
Barnard, Patrick [2 ]
Hummel, Michelle [1 ]
Stacey, Mark T. [1 ]
机构
[1] Univ Calif Berkeley, Dept Civil & Environm Engn, Berkeley, CA 94720 USA
[2] US Geol Survey, Pacific Coastal & Marine Sci Ctr, Santa Cruz, CA USA
基金
美国国家科学基金会;
关键词
infrastructure; dynamic mode decomposition; inverse method; sea level rise; coastal flooding; shoreline protection; DYNAMIC-MODE DECOMPOSITION; SAN-FRANCISCO BAY; EUROPEAN SHELF; TIDES; RISE; PATTERNS; IMPACT; INLET;
D O I
10.1002/2017JC012730
中图分类号
P7 [海洋学];
学科分类号
0707 ;
摘要
Sea level rise increases the risk of storms and other short-term water-rise events, because it sets a higher water level such that coastal surges become more likely to overtop protections and cause floods. To protect coastal communities, it is necessary to understand the interaction among multiday and tidal sea level variabilities, coastal infrastructure, and sea level rise. We performed a series of numerical simulations for San Francisco Bay to examine two shoreline scenarios and a series of short-term and long-term sea level variations. The two shoreline configurations include the existing topography and a coherent full-bay containment that follows the existing land boundary with an impermeable wall. The sea level variability consists of a half-meter perturbation, with duration ranging from 2 days to permanent (i.e., sea level rise). The extent of coastal flooding was found to increase with the duration of the high-water-level event. The nonlinear interaction between these intermediate scale events and astronomical tidal forcing only contributes similar to 1% of the tidal heights; at the same time, the tides are found to be a dominant factor in establishing the evolution and diffusion of multiday high water events. Establishing containment at existing shorelines can change the tidal height spectrum up to 5%, and the impact of this shoreline structure appears stronger in the low-frequency range. To interpret the spatial and temporal variability at a wide range of frequencies, Optimal Dynamic Mode Decomposition is introduced to analyze the coastal processes and an inverse method is applied to determine the coefficients of a 1-D diffusion wave model that quantify the impact of bottom roughness, tidal basin geometry, and shoreline configuration on the high water events.
引用
收藏
页码:9962 / 9979
页数:18
相关论文
共 43 条
  • [1] [Anonymous], 1996, OPEN CHANNEL FLOW
  • [2] Estimation of perturbations in robotic behavior using dynamic mode decomposition
    Berger, Erik
    Sastuba, Mark
    Vogt, David
    Jung, Bernhard
    Ben Amor, Heni
    [J]. ADVANCED ROBOTICS, 2015, 29 (05) : 331 - 343
  • [3] A modeling-based analysis of the flooding associated with Xynthia, central Bay of Biscay
    Bertin, Xavier
    Li, Kai
    Roland, Aron
    Zhang, Yinglong J.
    Breilh, Jean Francois
    Chaumillon, Eric
    [J]. COASTAL ENGINEERING, 2014, 94 : 80 - 89
  • [4] Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
    Brunton, Bingni W.
    Johnson, Lise A.
    Ojemann, Jeffrey G.
    Kutz, J. Nathan
    [J]. JOURNAL OF NEUROSCIENCE METHODS, 2016, 258 : 1 - 15
  • [5] Subtidal water level variation controlled by river flow and tides
    Buschman, F. A.
    Hoitink, A. J. F.
    van der Vegt, M.
    Hoekstra, P.
    [J]. WATER RESOURCES RESEARCH, 2009, 45
  • [6] Long-term tidal level distribution using a wave-by-wave approach
    Castanedo, Sonia
    Mendez, Fernando J.
    Medina, Raul
    Abascal, Ana J.
    [J]. ADVANCES IN WATER RESOURCES, 2007, 30 (11) : 2271 - 2282
  • [7] Chatterjee A., 2004, CURRENT SCI, V78, P808
  • [8] Variants of Dynamic Mode Decomposition: Boundary Condition, Koopman, and Fourier Analyses
    Chen, Kevin K.
    Tu, Jonathan H.
    Rowley, Clarence W.
    [J]. JOURNAL OF NONLINEAR SCIENCE, 2012, 22 (06) : 887 - 915
  • [9] Deltares, 2016, D FLOW FLEX MESH
  • [10] Doehring C., 2016, SFEI CONTRIB